train_lda_topics

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full burden for behavioral disclosure. It does not mention side effects (e.g., whether training modifies documents), performance implications, output persistence, or required permissions. Basic actions are described but key behavioral traits are omitted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences with no redundant information. Each sentence adds value: specifying the task, the method, and the output. Front-loaded with the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description adequately mentions return values (topics with top words and document assignments). However, it does not explain how to use the output or interpret results. Given the complexity of LDA and having three optional parameters, more context on typical usage would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides 100% description coverage for all three parameters (`num_topics`, `max_iter`, `random_state`). The tool description adds no additional meaning beyond what the schema already offers, meeting the baseline for good schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it trains an LDA topic model on documents, discovering latent topics, and returns topics with top words and document assignments. This is specific and distinguishes it from sibling tools like train_bertopic or train_nmf_topics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for probabilistic topic modeling but does not explicitly state when to use LDA over alternatives (e.g., BERTopic, NMF) or provide context for when not to use it. No exclusions or comparisons are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.